Fractional flow reserve (ffr) index with adaptive boundary condition parameters

ABSTRACT

A method includes obtaining a boundary condition estimate. The boundary condition estimate includes at least an estimated outlet resistance of a vessel with a stenosis. The method further includes correcting the boundary condition estimate based on a severity of the stenosis, thereby creating a corrected boundary condition. The method further includes determining an FFR index based on the corrected boundary condition. The method further includes displaying the FFR index. A computing system includes a computer readable storage medium with instructions that iteratively determine at least an FFR index based on a severity of a stenosis of a vessel. The computing system further includes a computer processor that processes the instructions and generates the FFR index based on the severity of the stenosis of the vessel.

The following generally relates to the fractional flow reserve (FFR) index and more particularly to determining an FFR index using adaptive boundary conditions, and is described with particular application to computed tomography (CT). However, the following is also amenable to other imaging modalities including X-ray, magnetic resonance imaging (MRI), and/or other imaging modalities.

The FFR index is an index of the functional severity of a coronary stenosis that is calculated from pressure measurements made during coronary arteriography and is defined as the distal blood pressure (behind a stenosis) relative to the proximal pressure (close to the Ostium) under hyperaemic conditions. That is, the FFR index expresses the maximal flow down a vessel in the presence of a stenosis compared to the maximal flow in the hypothetical absence of the stenosis. The FFR value is an absolute number between 0 and 1, where a value 0.50 indicates that a given stenosis causes a 50% drop in blood pressure, and facilitates diagnosis of the extent of a stenosis.

The FFR index has been measured using a pressure wire to obtain the blood pressure before and after the stenosis. For example, during coronary catheterization, a catheter is inserted into the femoral or radial arteries using a sheath and guide wire. A sensor, affixed to the tip of the catheter, is positioned at the stenosis. The catheter and hence the sensor is pulled back and the sensor senses pressure, temperature and flow, which are recorded, across the stenosis, during conditions promoted by various agents that effect vessel geometry, compliance and resistance, and/or other characteristics. Unfortunately, this approach is costly and minimally invasive, exposing the patient to health risks.

A non-invasive approach to estimating the FFR index is through computational fluid dynamic (CFD) simulations in which blood flow and pressure through the coronaries is simulated. With this approach, the boundary conditions (i.e., resistance) outside the extracted geometry are not well-defined. One approach uses a lumped model with a constant resistor at the coronary outlets to estimate the boundary conditions. However, in actuality, this resistance is not a constant, and it does impact the FFR. As such, this approach may lead to an estimation error.

Aspects described herein address the above-referenced problems and others.

As described herein, an FFR is estimated for a vessel with a stenosis using boundary conditions. Initial boundary conditions are estimated without taking into account a severity of a vessel stenosis and used to determine an FFR. The severity is not known prior to determining the FFR. Then, the boundary conditions are corrected, iteratively, based on the determined FFR, which indicates the severity of a vessel stenosis. A final FFR is output upon satisfying stopping criterion. The final FFR will be more accurate than an FFR that does not take into account the severity of a vessel stenosis.

In one aspect, a method includes obtaining a boundary condition estimate. The boundary condition estimate includes at least an estimated outlet resistance of a vessel with a stenosis. The method further includes correcting the boundary condition estimate based on a severity of the stenosis, thereby creating a corrected boundary condition. The method further includes determining an FFR index based on the corrected boundary condition. The method further includes displaying the FFR index.

In another aspect, a computing system includes a computer readable storage medium with instructions that iteratively determine at least an FFR index based on a severity of a stenosis of a vessel. The computing system further includes a computer processor that processes the instructions and generates the FFR index based on the severity of the stenosis of the vessel.

In another aspect, a computer readable storage medium is encoded with computer readable instructions, which, when executed by a computer processor of a computing system, causes the computer processor to: estimate an outlet resistance boundary condition estimate for a vessel with a stenosis based on a geometry of the vessel and a flow parameter of the vessel, iteratively correct the boundary condition estimate based on a severity of the stenosis; and determine an FFR index based on the corrected boundary condition.

The invention may take form in various components and arrangements of components, and in various steps and arrangements of steps. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention.

FIG. 1 schematically illustrates a computing system, which is configured to determine at least an FFR index, in connection with an imaging system.

FIG. 2 illustrates an example of an index determiner of FIG. 1.

FIG. 3 illustrates an example method for determining an FFR index.

The following describes an iterative approach for determining an FFR index. This approach includes initially assuming all vessels are healthy and using a CFD simulation with a resistance boundary condition estimate to determine an FFR index. Then, the resistance boundary condition is iteratively corrected according to the stenosis severity, which is estimated by the FFR index from the previous iteration. A subsequent FFR index is then determined based on the corrected boundary condition. The iterative process of correcting resistance boundary condition and calculating another FFR index continues until stopping criterion is satisfied. A final FFR index is then output.

FIG. 1 schematically illustrates an imaging system 100 such as a CT scanner. The imaging system 100 includes a generally stationary gantry 102 and a rotating gantry 104, which is rotatably supported by the stationary gantry 102 and rotates around an examination region 106 about a z-axis. A subject support 108, such as a couch, supports an object or subject in the examination region 106.

A radiation source 110, such as an x-ray tube, is rotatably supported by the rotating gantry 104, rotates with the rotating gantry 104, and emits radiation that traverses the examination region 106. A radiation sensitive detector array 112 subtends an angular arc opposite the radiation source 110 across the examination region 106. The radiation sensitive detector array 112 detects radiation traversing the examination region 106 and generates a signal indicative thereof for each detected photon. A reconstructor 114 reconstructs the projection, generating volumetric image data indicative of a scanned portion of a subject or object located in the examination region 106. A general-purpose computing system or computer serves as an operator console 116. The console 116 includes a human readable output device such as a monitor and an input device such as a keyboard, mouse, etc. Software resident on the console 116 allows the operator to interact with and/or operate the scanner 100 via a graphical user interface (GUI) or otherwise.

An index determiner 118 is configured to at least process image data representing a vessel(s) (e.g., coronary arteries, cerebral artery, etc.) with a stenosis and determine an FFR index. As described in greater detail below, in one non-limiting instance, the index determiner 118 uses an iterative approach to correct an initial boundary conditions estimate (e.g., resistance) based on a severity of a stenosis. As a result, inaccuracy from an FFR estimation that does not iteratively correct the boundary conditions, such as an FFR estimation that utilizes a lumped model with a constant resistance at the coronary outlets, can be mitigated.

In the illustrated example, the index determiner 118 is implemented with one or more computer processors 120 (e.g., a central processing unit or CPU, a microprocessor, etc.), of a computing system 122, that execute one or more computer readable instructions 124 stored in one or more computer readable storage mediums 126 (which excludes transitory medium) such as physical memory and/or other non-transitory storage medium. The processor(s) 120 may additionally or alternatively execute one or more computer readable instructions carried by a carrier wave, a signal and/or other transitory medium.

FIG. 2 illustrates an example of the index determiner 118.

A tissue of interest (TOI) identifier 202 obtains, as input, subject data, including image data representative of tissue of interest (TOI) 204 and identifies the tissue of interest 204 in the image data. The tissue of interest 204 can be predetermined or identified by a signal indicative of a user selected tissue of interest, a default tissue of interest, etc. The TOI identifier 202 can employ automatic and/or manual approaches to identify the tissue of interest. An example of tissue of interest is tubular tissue such as a vessel with a stenosis.

A TOI geometry extractor 206 extracts geometrical information from the identified tissue of interest. The TOI identifier 202 can employ automatic and/or manual approaches to extract the geometrical information. By way of example, the extraction may include employing segmentation with active-contours and level-sets tuned for coronary vessels where the tissue of interest is the coronary vessels, followed by optional additional manual editing to produce high quality segmentation. From this and/or other extraction, an effective diameter at the ostium D_(o) and/or other tissue geometry can be determined.

A parameter determiner 208 determines at least one parameter based on the subject data. For example, in the context of vessel stenosis, the parameter determiner 208 can determine an inlet flow-rate Q_(o) (i.e., flow rate at the ostium). This can be achieved based on subject data such as weight, body mass index (BMI), gender, age, blood test results, anatomical imaging data (e.g., myocardium mass and estimated stroke-volume), and/or subject data.

A boundary condition estimator 210 estimates at least one boundary condition (e.g., flow rate Q, average velocity, resistance, etc. of vessel outlets) based on the geometry extracted (e.g., diameter at the ostium D_(o)) by the TOI geometry extractor 206 and the parameter determined (e.g., the inlet flow-rate Q_(o)) by the parameter determiner 208. The boundary condition estimator 210 can estimate the boundary, as described in application Ser. No. 61/722,814, filed on Nov. 6, 2012, and entitled “FRACTIONAL FLOW RESERVE (FFR) INDEX,” the entirety of which is incorporated herein by reference.

A boundary condition corrector 212 corrects the resistance boundary condition based on EQUATION 1:

EQUATION 1:

R ^(i) =R ^(i-1) *C(FFR ^(i-1)),

where R^(i) is a current outlet resistance, R^(i-1) is a previously determined outlet resistance, and C(FFR^(i-1)) is a correction factor. The correction factor C is a known function (or look-up-table) and is defined for all 0<FFR<=1 and monotonically decreases and assumes the value C(1)=1, so a perfectly healthy vessel attains the resistance R^(i-1). For the first iteration, or i=1, the boundary condition corrector 212 utilizes an initial FFR 214, which, in this example, is FFR⁰=1.

A CFD processor 216 performs a computational fluid dynamic (CFD) simulation, for example, using partial-differential-equations. Generally, CFD is a fluid mechanics approach that uses numerical methods and/or algorithms to solve and analyze problems that involve fluid flows. The CFD processor 216 performs the calculations with surfaces defined by boundary conditions determined by the boundary condition corrector 212. However, other boundary conditions can also be employed. The output, in one instance, includes full volumetric information of pressure and velocity at all points.

An FFR index determiner 218 determines an FFR index based on the CFD results. This includes determining the FFR index based on the corrected boundary conditions. Suitable approaches to determine an FFR index using computational fluid dynamics is described in, but not limited to, Taylor C A, Figueroa C A, “Patient-Specific Modeling of Cardiovascular Mechanics,” Annual Review of Biomedical Engineering, Vol. 11: 109-134, August 2009, and Huo Y, Kassab G S, “Intraspecific scaling laws of vascular trees,” J. R. Soc. Interface, 15 Jun. 2011.

Decision logic 220 determines whether another iteration is performed. Stopping criteria 222 is set, for example, to a small constant ε<<1, which defines the tradeoff between accuracy (low value) and convergence time (high value). In one instance, ε≈0.01. In a variation, the stopping criterion 222 alternatively or additionally includes a predetermined amount of time. The decision logic 218 compares the current FFR^(i) index with the previous FFR^(i-1) index. If |FFR^(i)−FFR^(i-1)<ε then the current FFR^(i) is set as the final FFR index. Otherwise, i=i+1, and a next iteration is performed for i=i+1.

The initial FFR of FFR⁰=1 assumes, for a first iteration, that all vessels are healthy. However, if a vessel is not healthy, this will be detected after the first simulation, as the simulated resistance will be lower than what it should be (i.e., unhealthy vessels have higher resistance). Therefore, the FFR of the first iteration will show exaggerated low FFR values, which will be corrected in later iterations. If the vessel is healthy, then the assumption is true, and the CFD should produce realistic results.

For example, R^(i), for i=1 with FFR⁰=1 and C(1)=1, using Equation 1, is R° . If the vessel is unhealthy, this R^(i) will be higher than it should be. As a result, the computation of the next FFR, i.e., FFR¹, with R¹=R⁰ will result in a difference (|FFR¹−FFR⁰|) that is much greater than E and another iteration will be performed. In the next iteration, i=2, R², using Equation 1 and based on FFR¹ which is less than 1, will be less than R⁰ and closer to the actual resistance. The computation of the next FFR, i.e., FFR², with R² will result in a difference (|FFR²−FFR¹|) that is smaller than (|FFR¹−FFR⁰|).

However, if the vessel is healthy, R^(i), for i=1, will be close to the actual resistance based on the assumption, and |FFR¹−FFR⁰| will be less than E and another iteration will not be performed; another iteration is performed only if |FFR²−FFR¹| is larger than E. Thus, an incorrect initial assumption of a healthy vessel will be detected based on the difference between the FFR computed based on the initial assumption and the next determined FFR and will be corrected through performing one or more subsequent iterations.

The FFR index can be displayed, stored, conveyed to another device, etc.

The particular adaptive (pressure/FFR dependent) resistance model employed is not limited to that discussed above. Another suitable resistance model is described next.

A general approximation to the FFR index is shown in EQUATION 2:

$\begin{matrix} {{FFR} = \frac{R}{R + r^{\prime}}} & {{EQUATION}\mspace{14mu} 2} \end{matrix}$

where R and r denote the microvascular and stenosis resistances respectively. Here, R is combined venues resistance with the larger micro arteriolar resistance. The stenosis resistance, as a function of the microvascular resistance and FFR index, can be represented as shown in EQUATION 3:

$\begin{matrix} {R = {{r\left( {\frac{1}{FFR} - 1} \right)}.}} & {{EQUATION}\mspace{14mu} 3} \end{matrix}$

An FFR index with variation εR in the distal resistance can be derived as shown in EQUATIONS 4 and 5:

$\begin{matrix} {{\hat{FFR} = {\frac{R + {ɛ\; R}}{R + {ɛ\; R} + r} = \frac{R + {ɛ\; R}}{R + {ɛ\; R} + {R\left( {\frac{1}{FFR} - 1} \right)}}}},{and}} & {{EQUATION}\mspace{14mu} 4} \\ {\hat{FFR} = {\frac{1 + ɛ}{ɛ + \frac{1}{FFR}} = {\frac{{FFR}\left( {1 + ɛ} \right)}{{{FFR}\; ɛ} + 1}.}}} & {{EQUATION}\mspace{14mu} 5} \end{matrix}$

Assuming a piecewise linear relation between the FFR and the peripheral resistance, an optimal parameter a for each segment can be found. For two segments with common point (th, RO), a linear equation at segment [0, th] can be represented as shown in EQUATION 6:

EQUATION 6:

R0+εR0=FFR·α+B, and

and a linear equation at segment [th, 1] can be represented as shown in EQUATION 7:

EQUATION 7:

R0+εR0=FFR·β+B.

Since the analysis is the same, only segment [0, th] is described in detail herein. The relative variation in the microvascular resistance with respect to the model is shown in EQUATION 8:

$\begin{matrix} {ɛ = {\frac{\left( {{FFR} - {th}} \right) \cdot \alpha}{R\; 0}.}} & {{EQUATION}\mspace{14mu} 8} \end{matrix}$

Parameter optimization (where J=RMSE), is shown in EQUATIONS 9, 10, and 11:

$\begin{matrix} {\mspace{79mu} {{J = {\sum\limits_{all}\left( {{FFR}_{GT} - \frac{{FFR}\left( {1 + ɛ} \right)}{{{FFR}\; ɛ} + 1}} \right)^{2}}},}} & {{EQUATION}\mspace{14mu} 9} \\ {\mspace{85mu} {{ɛ = \frac{\left( {{FFR} - {th}} \right) \cdot \alpha}{R\; 0}},{and}}} & {{EQUATION}\mspace{14mu} 10} \\ {{\frac{J}{\alpha} = {2{\sum\limits_{all}{\left( {{FFR}_{GT} - \frac{{FFR}\left( {1 + ɛ} \right)}{{{FFR}\; ɛ} + 1}} \right) \cdot \left( \frac{{FFR}\left( {{FFR} - 1} \right)}{\left( {{{FFR}\; ɛ} + 1} \right)^{2}} \right) \cdot \frac{\left( {{FFR} - {th}} \right)}{R\; 0}}}}},{and},} & {{EQUATION}\mspace{14mu} 11} \end{matrix}$

the optimal parameter a for each segment can be found using a gradient decent as shown in EQUATION 12:

$\begin{matrix} {\left. \alpha\leftarrow{\alpha - {{step} \cdot \frac{J}{\alpha}}} \right.,} & {{EQUATION}\mspace{14mu} 12} \end{matrix}$

FIG. 3 illustrates an example method for determining an FFR index.

At 302, a vessel of interest of a subject is scanned.

At 304, a sub-portion of the vessel having a stenosis is identified in the image data from the scan.

At 306, geometrical information, such as a diameter, a radius, etc., is extracted from the vessel.

At 308, at least one property such as inlet flow-rate, etc. of the vessel is determined.

At 310, at least one boundary condition (e.g., resistance) for the vessel is estimated, e.g., based on the extracted geometrical information and the at least one parameter.

At 312, the boundary condition is refined based on a previous FFR index. For a first iteration, the previous FFR index is set to an initial FFR index, which assumes all vessels are healthy. For each subsequent iteration, the previous FFR index is the corresponding generated preceding FFR index.

At 314, a computational fluid dynamic (CFD) simulation is performed based on the refined boundary condition.

At 316, a current FFR index is generated based on a result of the CFD.

At 318, the current FFR index is compared with a previous FFR index.

Likewise, for the first iteration, the previous FFR index is set to an initial FFR index, which assumes all vessels are healthy, and, for each subsequent iteration, the previous FFR index is the corresponding generated succeeding FFR index.

At 320, if a difference between the current FFR index and the previous FFR index does not satisfy stopping criterion, then acts 312-318 are repeated.

If at 320, the difference between the current FFR index and the previous FFR index satisfies the stopping criterion, the current FFR index is output at 322 as the final FFR index.

The above may be implemented by way of computer readable instructions, encoded or embedded on computer readable storage medium, which, when executed by a computer processor(s), cause the processor(s) to carry out the described acts. Additionally or alternatively, at least one of the computer readable instructions is carried by a signal, carrier wave or other transitory medium. It is to be appreciated that the ordering of the above acts is not limiting. As such, other orderings are contemplated herein. In addition, one or more acts may be omitted and/or one or more additional acts may be included.

The invention has been described with reference to the preferred embodiments. Modifications and alterations may occur to others upon reading and understanding the preceding detailed description. It is intended that the invention be constructed as including all such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof 

1. A method, comprising: obtaining a boundary condition estimate, wherein the boundary condition estimate includes at least an estimated outlet resistance of a vessel with a stenosis; correcting the boundary condition estimate based on a severity of the stenosis, thereby creating a corrected boundary condition; determining an FFR index based on the corrected boundary condition; and displaying the FFR index.
 2. The method of claim 1, further comprising: correcting the boundary condition estimate using an iterative correction.
 3. The method of claim 2, the correcting, comprising: generating a first corrected boundary condition based on the boundary condition estimate and a correction factor, wherein the correction factor is a function of an FFR of a previous iteration; performing a first CFD simulation using the first corrected boundary condition; and generating a first FFR index based a result of the first CFD simulation.
 4. The method of claim 3, wherein the correction factor is one of a function or a look up table.
 5. The method of claim 3, wherein the correction factor is defined for all 0<FFR<=1.
 6. The method of claim 3, wherein the correction factor monotonically decreases.
 7. The method of claim 3, wherein, for a first iteration, the FFR of the previous iteration is a predetermined initial condition with a value of
 1. 8. The method of claim 7, further comprising: comparing the first FFR index with the value of 1; performing a second iteration in response to a difference between the first FFR index and the value of 1 is greater than a predetermined threshold value.
 9. The method of claim 3, wherein, for a subsequent iteration, the FFR of the previous iteration is the FFR for the immediately preceding iteration.
 10. The method of claim 9, further comprising: comparing the first FFR index with the FFR for the immediately preceding iteration; performing a second iteration in response to a first difference between the first FFR index and the FFR for the immediately preceding iteration is greater than a predetermined threshold value.
 11. The method of claim 10, further comprising: generating a second corrected boundary condition based on the first corrected boundary condition and the correction factor; performing a second CFD simulation using the second corrected boundary condition; and generating a second FFR index based a result of the second CFD simulation.
 12. The method of claim 11, further comprising: comparing the second FFR index with the first FFR; performing a third iteration in response to a second difference between the second FFR index and the first FFR being greater than the predetermined threshold value.
 13. The method of claim 11, further comprising: setting the FFR index equal to the first FFR index in response to the difference between the first FFR index and the FFR for the immediately preceding iteration satisfying the predetermined threshold value.
 14. The method of claim 1, further comprising: estimating the boundary condition estimate based on a geometry of the vessel and flow parameter of the vessel.
 15. A computing system, comprising: a computer readable storage medium with instructions that iteratively determine at least an FFR index based on a severity of a stenosis of a vessel; and a computer processor that processes the instructions and generates the FFR index based on the severity of the stenosis of the vessel.
 16. The computing system of claim 15, wherein the computer processor estimates a boundary condition estimate that includes an estimated outlet resistance of the vessel with the stenosis, corrects the boundary condition estimate based on the severity of the stenosis, and determines the FFR index based on the corrected boundary condition.
 17. The computing system of claim 16, wherein the computer processor corrects the boundary condition estimate based on previously determined FFR index.
 18. The computing system of claim 17, wherein, for a first iteration, the previously determined FFR index is a predetermined initial condition.
 19. The computing system of claim 18, wherein the predetermined initial condition assumes the vessel is healthy and does not include the stenosis.
 20. The computing system of claim 17, wherein, for a second or subsequent iteration, the previously determined FFR index is a FFR index determined in the immediately preceding iteration.
 21. The computing system of claim 17, wherein the previously determined FFR index indicates the severity of the stenosis of the vessel.
 22. The computing system of claim 17, wherein the processor generates a first corrected boundary condition based on the boundary condition and a correction factor, performs a first CFD simulation using the first corrected boundary condition; and generates a first FFR index based a result of the first CFD simulation.
 23. The computing system of claim 22, wherein the processor generates a second corrected boundary condition based on the first corrected boundary condition and the correction factor, performs a second CFD simulation using the second corrected boundary condition; and generates a second FFR index based a result of the second CFD simulation.
 24. The computing system of claim 22, wherein the correction factor is a function of the previously determined FFR index.
 25. A computer readable storage medium encoded with computer readable instructions, which, when executed by a processor of a computing system, causes the processor to: estimate an outlet resistance boundary condition estimate for a vessel with a stenosis based on a geometry of the vessel and a flow parameter of the vessel; iteratively correct the boundary condition estimate based on a severity of the stenosis; and determine an FFR index based on the corrected boundary condition. 